Lip segmentation and tracking based on Chan-Vese model

Nasuha, Aris; Sardjono, Tri Arief; Purnomo, Mauridhi Hery

doi:10.1109/iciteed.2013.6676230

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Leung et al [24] applied fuzzy color clustering. There are other color spaces that are used to display lip color and background color differences, such as YCbCr [25], NTSC [26], CIE-Lab [27][28][29][30], and bi-color space [31].…”

Section: Introductionmentioning

confidence: 99%

Lip segmentation using automatic selected initial contours based on localized active contour model

Liu

2018

J Image Video Proc.

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With the rapid development of artificial intelligence and the increasing popularity of smart devices, human-computer interaction technology has become a multimedia and multimode technology from being computer-focused to people-centered. Among all ways of human-computer interactions, using language to interact with machines is the most convenient and efficient one. However, the performance of audio speech recognition systems is not satisfied in a noisy environment. Thus, more and more researchers focus their works on visual lip reading technology. By extracting lip movement features of speakers rather than audio features, visual lip reading systems can get superior results when noises and interferences exist. Lip segmentation plays an important role in a visual lip reading system, since the segmentation result is crucial to the final recognition accuracy. In this paper, we propose a localized active contour model-based method using two initial contours in a combined color space. We apply illumination equalization to original RGB images to decrease the interference of uneven illumination. A combined color space consists of the U component in CIE-LUV color space and the sum of C2 and C3 components of the image after discrete Hartley transform. We select a rhombus as the initial contour of a closed mouth, because it has a similar shape to a closed lip. For an open mouth, we utilize a combined semi-ellipse as the initial contours of both outer and inner lip boundaries. After attaining the results of each color component separately, we merge them together to obtain the final segmentation result. From the experiment, we can conclude that this method can get better segmentation results compared with the method using a circle as the initial contour to segment gray images and images in combined color space, especially for open mouth. An extremely obvious advantage of this method is the results of open mouth excluding internal information of mouth such as teeth, black holes, and tongue, because of the introduction of the inner initial contour.

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Section: Introductionmentioning

confidence: 99%

Lip segmentation using automatic selected initial contours based on localized active contour model

Liu

2018

J Image Video Proc.

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Visual Speech Recognition for Daily Indonesian Words Based on Combination of Double Difference and Image Projection Method

Nasuha

Arifin

Irmawati

et al. 2019

J. Phys.: Conf. Ser.

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In visual speech recognition, there are two approaches to lip feature extraction, which are based on appearance and shape. The appearance-based approach is usually better, because it provides visual features that not only include lip structure but also the visibility of the teeth and tongue. However, the disadvantage of this approach is that it produces too many features. Integration of double difference and horizontal-vertical image projection, is part of the appearance approach, in addition to using image projection as dimensional reduction. In previous study, the method has succeeded in recognizing 5 daily indonesian words, with data in form of videos recorded inside the room. In this study, we used the same words with 5 additional new words recorded outside the room. MLP (Multi Layer Perceptron) and SVM (Support Vector Machine) are used as classifiers. The word recognition process is evaluated using 10-fold cross validation. The method tested reached 88.92% on classification accuracy and 0.9948 on AUC (Area Under ROC Curve).

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Lip segmentation and tracking based on Chan-Vese model

Cited by 2 publications

References 20 publications

Lip segmentation using automatic selected initial contours based on localized active contour model

Lip segmentation using automatic selected initial contours based on localized active contour model

Visual Speech Recognition for Daily Indonesian Words Based on Combination of Double Difference and Image Projection Method

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